S
Sophie Lèbre
Researcher at Centre national de la recherche scientifique
Publications - 41
Citations - 2267
Sophie Lèbre is an academic researcher from Centre national de la recherche scientifique. The author has contributed to research in topics: Dynamic Bayesian network & Bayesian network. The author has an hindex of 12, co-authored 40 publications receiving 1985 citations. Previous affiliations of Sophie Lèbre include Imperial College London & University of Montpellier.
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Wisdom of crowds for robust gene network inference
Daniel Marbach,James C. Costello,Robert Küffner,Nicole M. Vega,Robert J. Prill,Diogo M. Camacho,Kyle R. Allison,Andrej Aderhold,Richard Bonneau,Yukun Chen,James J. Collins,Francesca Cordero,Martin Crane,Frank Dondelinger,Mathias Drton,Roberto Esposito,Rina Foygel,Alberto de la Fuente,Jan Gertheiss,Pierre Geurts,Alex Greenfield,Marco Grzegorczyk,Anne-Claire Haury,Benjamin Holmes,Torsten Hothorn,Dirk Husmeier,Vân Anh Huynh-Thu,Alexandre Irrthum,Manolis Kellis,Guy Karlebach,Sophie Lèbre,Vincenzo De Leo,Aviv Madar,Subramani Mani,Fantine Mordelet,Harry Ostrer,Zhengyu Ouyang,Ravi Pandya,Tobias Petri,Andrea Pinna,Christopher S. Poultney,Serena Rezny,Heather J. Ruskin,Yvan Saeys,Ron Shamir,Alina Sîrbu,Mingzhou Song,Nicola Soranzo,Alexander Statnikov,Gustavo Stolovitzky,Nicci Vega,Paola Vera-Licona,Jean-Philippe Vert,Alessia Visconti,Haizhou Wang,Louis Wehenkel,Lukas Windhager,Yang Zhang,Ralf Zimmer +58 more
TL;DR: A comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data defines the performance, data requirements and inherent biases of different inference approaches, and provides guidelines for algorithm application and development.
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Statistical inference of the time-varying structure of gene-regulation networks
Sophie Lèbre,Sophie Lèbre,Jennifer Becq,Jennifer Becq,Frédéric Devaux,Michael P. H. Stumpf,Gaëlle Lelandais,Gaëlle Lelandais +7 more
TL;DR: ARTIVA does recover essential temporal dependencies in biological systems from transcriptional data, and provide a natural starting point to learn and investigate their dynamics in greater detail.
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Inferring dynamic genetic networks with low order independencies.
TL;DR: In this article, a low-order conditional dependence graph (LODG) is proposed for dynamic Bayesian networks, which makes it possible to face with a number of time measurements n much smaller than the number of genes p.
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Non-homogeneous dynamic Bayesian networks with Bayesian regularization for inferring gene regulatory networks with gradually time-varying structure
TL;DR: A semi-flexible model based on a piecewise homogeneous dynamic Bayesian network regularized by gene-specific inter-segment information sharing is explored and different choices of prior distribution and information coupling are explored and their performance on synthetic data is evaluated.